EN
Feature selection of Thyroid disease using Deep Learning: A Literature survey
Öz
The thyroid hormone, which is secreted by the thyroid gland, helps regulate the body's metabolism. Thyroid disorders can range from a small, harmless goiter that does not need to be treated for life-threatening cancer. The most common thyroid problems include abnormal production of thyroid hormones. Overproduction of the thyroid leads to the thyroid and inadequate hormone production leads to hypothyroidism. Although the effects can be unpleasant or uncomfortable, many thyroid problems can be managed well if they are timely diagnosed and treated correctly. In this paper, the diagnosis of thyroid disease is investigated using deep learning based on the imperialist competitive algorithm feature selection method.
Anahtar Kelimeler
Kaynakça
- Siti F, Shurehdeli MA, Teshneh Lab M. 2008. Diagnosis of thyroid disease using probabilistic neural networks and Genetic Algorithm: 2nd Joint Congress on Fuzzy and Intelligent Systems. Iran.
- Razmjooy N, Musavi BS, Soleymani F. 2013. A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation. Mathematical and Computer Modelling 57(3): 848-856.
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- Seaver N. 2014. Media in Transition 8, Cambridge, MA, April.Knowing algorithms.Department of Anthropology, UC Irvine Intel Science and Technology Center for Social Computing. 1:23-28
- Memari A, Robiah A, Abdul Rahim Abd. 2017. Metaheuristic Algorithms: Guidelines for Implementation. Journal of Soft Computing and Decision Support Systems. 4: 1-6.
- Rajpurohit J, Sharma TK, Abraham A, Vaishali. 2017. Glossary of Metaheuristic Algorithms. International Journal of Computer Information Systems and Industrial Management Applications, 9: 181-205.
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Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Derleme
Yayımlanma Tarihi
1 Temmuz 2020
Gönderilme Tarihi
28 Şubat 2020
Kabul Tarihi
17 Mart 2020
Yayımlandığı Sayı
Yıl 2020 Cilt: 3 Sayı: 3
APA
Mehrno, A., Oktaş, R., & Odabas, M. S. (2020). Feature selection of Thyroid disease using Deep Learning: A Literature survey. Black Sea Journal of Engineering and Science, 3(3), 109-114. https://doi.org/10.34248/bsengineering.695904
AMA
1.Mehrno A, Oktaş R, Odabas MS. Feature selection of Thyroid disease using Deep Learning: A Literature survey. BSJ Eng. Sci. 2020;3(3):109-114. doi:10.34248/bsengineering.695904
Chicago
Mehrno, Amir, Recai Oktaş, ve Mehmet Serhat Odabas. 2020. “Feature selection of Thyroid disease using Deep Learning: A Literature survey”. Black Sea Journal of Engineering and Science 3 (3): 109-14. https://doi.org/10.34248/bsengineering.695904.
EndNote
Mehrno A, Oktaş R, Odabas MS (01 Temmuz 2020) Feature selection of Thyroid disease using Deep Learning: A Literature survey. Black Sea Journal of Engineering and Science 3 3 109–114.
IEEE
[1]A. Mehrno, R. Oktaş, ve M. S. Odabas, “Feature selection of Thyroid disease using Deep Learning: A Literature survey”, BSJ Eng. Sci., c. 3, sy 3, ss. 109–114, Tem. 2020, doi: 10.34248/bsengineering.695904.
ISNAD
Mehrno, Amir - Oktaş, Recai - Odabas, Mehmet Serhat. “Feature selection of Thyroid disease using Deep Learning: A Literature survey”. Black Sea Journal of Engineering and Science 3/3 (01 Temmuz 2020): 109-114. https://doi.org/10.34248/bsengineering.695904.
JAMA
1.Mehrno A, Oktaş R, Odabas MS. Feature selection of Thyroid disease using Deep Learning: A Literature survey. BSJ Eng. Sci. 2020;3:109–114.
MLA
Mehrno, Amir, vd. “Feature selection of Thyroid disease using Deep Learning: A Literature survey”. Black Sea Journal of Engineering and Science, c. 3, sy 3, Temmuz 2020, ss. 109-14, doi:10.34248/bsengineering.695904.
Vancouver
1.Amir Mehrno, Recai Oktaş, Mehmet Serhat Odabas. Feature selection of Thyroid disease using Deep Learning: A Literature survey. BSJ Eng. Sci. 01 Temmuz 2020;3(3):109-14. doi:10.34248/bsengineering.695904